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Most techniques for computer classification of multispectral scanner data involve a "single-stage" approach in which every pixel in the data is classified in a single step, using a single set of training statistics and a single set of wave length bands. Hierarchical classifiers, on the other hand, involve a sequence of classification steps, each of which can involve a different wavelength band or combination of wavelength bands. In addition, at each step in the classification process only one spectral class or a specific group of spectral classes are separated from all other classes in the data. Since a relatively small number of wave length bands are involved at each step, and (after the initial step) only a portion of the data is being classified at each step, such hierarchical classifiers are computationally very efficient. However, as compared to single stage classifiers, the effectiveness of hierarchical classifiers in terms of classification accuracy is not clear, especially when dealing with multitemporal data sets. In this study, Landsat-I MSS data sets obtained in June 1971 and February 1974 over the Monroe Reservoir and Hoosier National Forest in central Indiana were used. After digitally registering the two data sets, four classification procedures were compared. The first consisted of a standard single stage maximum likelihood classification using an eight channel training statistics deck (four wavelength bands from each two dates). The second utilized the 4 best channels of the 8 available. The third involved the Layered hierarchical classifier and the same eight channel training statistics. The fourth approach utilized the Layered classifier again, but the data from the two dates were treated independently for the purpose of developing training statistics. The results indicate that the Layered classifier is a more effective and efficient approach for classification of multitemporal/multispectral scanner data. The classification accuracies were relatively high for all four classifications, but the Layered classifier required only one third of the CPU time used in the single stage classification.

The launch of the Landsat-4 satellite in July 1982 provided the first full coverage from space of the .4-12 µm spectrum of the earth scene. In addition to the green, red, and near IR bands of the MSS, the TM provides a band in the blue, two in the middle IR, and one thermal IR. The paper describes spectral class analysis of coincident MSS and TM data to evaluate the contribution of the additional TM bands. In addition, various classifiers are available which were applied to the TM data. In the spectral class analysis, twice the number of separable classes was found in the TM data compared to the MSS data.

Validation, a crucial stage in the development cycle of embedded systems, is normally carried out using static analysis based on scheduling techniques. In high performance embedded systems, where several tasks with high computing requirements are working on input and output signals with high sampling rates, parallel and distributed processing is a valuable design alternative in order for the system to achieve the fulfillment of its real-time constraints. When the validation of parallel and distributed embedded systems is considered, many simplifications are made in order to make analysis tractable. This means that even if the system can be statically validated, the real behaviour of the system in execution may be different enough from its theoretical behaviour to make it invalid. Thus, conservative designs that lead to over-dimensioned systems with partially wasted resources are commonly adopted. Although static analysis is the only alternative in case of critical embedded systems, where the fulfillment must be always guaranteed, dynamic analysis, based on measurement, is an interesting alternative for validation of non-critical embedded systems. This paper describes a methodology for performance debugging of parallel and distributed embedded systems with non-critical end-to-end deadlines. The methodology is based on the measurement of a prototype of the system in execution and is supported by a behavioural model.